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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1-2,图片数据建模流程范例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 一，准备数据\n",
    "cifar2数据集为cifar10数据集的子集，只包括前两种类别airplane和automobile。\n",
    "\n",
    "训练集有airplane和automobile图片各5000张，测试集有airplane和automobile图片各1000张。\n",
    "\n",
    "cifar2任务的目标是训练一个模型来对飞机airplane和机动车automobile两种图片进行分类。\n",
    "\n",
    "我们准备的Cifar2数据集的文件结构如下所示。\n",
    "\n",
    "![](./data/cifar2.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在tensorflow中准备图片数据的常用方案有两种，第一种是使用tf.keras中的ImageDataGenerator工具构建图片数据生成器。\n",
    "\n",
    "第二种是使用tf.data.Dataset搭配tf.image中的一些图片处理方法构建数据管道。\n",
    "\n",
    "第一种方法更为简单，其使用范例可以参考以下文章。\n",
    "\n",
    "https://zhuanlan.zhihu.com/p/67466552\n",
    "\n",
    "第二种方法是TensorFlow的原生方法，更加灵活，使用得当的话也可以获得更好的性能。\n",
    "\n",
    "我们此处介绍第二种方法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf \n",
    "from tensorflow.keras import datasets,layers,models\n",
    "\n",
    "BATCH_SIZE = 100\n",
    "\n",
    "def load_image(img_path, size = (32,32)):\n",
    "    label = tf.constant(1,tf.int8) if tf.strings.regex_full_match(img_path,\".*/automobile/.*\") \\\n",
    "            else tf.constant(0,tf.int8)\n",
    "    img = tf.io.read_file(img_path)\n",
    "    img = tf.image.decode_jpeg(img) #注意此处为jpeg格式\n",
    "    img = tf.image.resize(img,size)/255.0\n",
    "    return(img,label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#使用并行化预处理num_parallel_calls 和预存数据prefetch来提升性能\n",
    "ds_train = tf.data.Dataset.list_files(\"./data/cifar2/train/*/*.jpg\") \\\n",
    "           .map(load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE) \\\n",
    "           .shuffle(buffer_size = 1000).batch(BATCH_SIZE) \\\n",
    "           .prefetch(tf.data.experimental.AUTOTUNE)  \n",
    "\n",
    "ds_test = tf.data.Dataset.list_files(\"./data/cifar2/test/*/*.jpg\") \\\n",
    "           .map(load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE) \\\n",
    "           .batch(BATCH_SIZE) \\\n",
    "           .prefetch(tf.data.experimental.AUTOTUNE)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "<Figure size 576x576 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "#查看部分样本\n",
    "from matplotlib import pyplot as plt \n",
    "\n",
    "plt.figure(figsize=(8,8)) \n",
    "for i,(img,label) in enumerate(ds_train.unbatch().take(9)):\n",
    "    ax=plt.subplot(3,3,i+1)\n",
    "    ax.imshow(img.numpy())\n",
    "    ax.set_title(\"label = %d\"%label)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([]) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 32, 32, 3) (100,)\n"
     ]
    }
   ],
   "source": [
    "for x,y in ds_train.take(1):\n",
    "    print(x.shape,y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 二，定义模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用Keras接口有以下3种方式构建模型：使用Sequential按层顺序构建模型，使用函数式API构建任意结构模型，继承Model基类构建自定义模型。\n",
    "\n",
    "此处选择使用函数式API构建模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 32, 32, 3)]       0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 30, 30, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 15, 15, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 11, 11, 64)        51264     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 1600)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 32)                51232     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 103,425\n",
      "Trainable params: 103,425\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "tf.keras.backend.clear_session() #清空会话\n",
    "\n",
    "inputs = layers.Input(shape=(32, 32, 3))\n",
    "x = layers.Conv2D(32, kernel_size=(3, 3))(inputs)\n",
    "x = layers.MaxPool2D()(x)\n",
    "x = layers.Conv2D(64, kernel_size=(5, 5))(x)\n",
    "x = layers.MaxPool2D()(x)\n",
    "x = layers.Dropout(rate=0.1)(x)\n",
    "x = layers.Flatten()(x)\n",
    "x = layers.Dense(32, activation='relu')(x)\n",
    "outputs = layers.Dense(1, activation='sigmoid')(x)\n",
    "\n",
    "model = models.Model(inputs=inputs, outputs=outputs)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 三，训练模型\n",
    "\n",
    "训练模型通常有3种方法，内置fit方法，内置train_on_batch方法，以及自定义训练循环。此处我们选择最常用也最简单的内置fit方法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "100/100 [==============================] - 7s 68ms/step - loss: 0.4263 - accuracy: 0.7936 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/10\n",
      "100/100 [==============================] - 6s 61ms/step - loss: 0.3092 - accuracy: 0.8675 - val_loss: 0.2500 - val_accuracy: 0.8970\n",
      "Epoch 3/10\n",
      "100/100 [==============================] - 6s 61ms/step - loss: 0.2462 - accuracy: 0.8992 - val_loss: 0.2355 - val_accuracy: 0.9050\n",
      "Epoch 4/10\n",
      "100/100 [==============================] - 6s 64ms/step - loss: 0.2107 - accuracy: 0.9162 - val_loss: 0.1863 - val_accuracy: 0.9270\n",
      "Epoch 5/10\n",
      "100/100 [==============================] - 6s 64ms/step - loss: 0.1819 - accuracy: 0.9296 - val_loss: 0.1981 - val_accuracy: 0.9180\n",
      "Epoch 6/10\n",
      "100/100 [==============================] - 6s 64ms/step - loss: 0.1627 - accuracy: 0.9381 - val_loss: 0.1695 - val_accuracy: 0.9310\n",
      "Epoch 7/10\n",
      "100/100 [==============================] - 6s 64ms/step - loss: 0.1373 - accuracy: 0.9450 - val_loss: 0.1655 - val_accuracy: 0.9295\n",
      "Epoch 8/10\n",
      "100/100 [==============================] - 6s 64ms/step - loss: 0.1211 - accuracy: 0.9552 - val_loss: 0.1598 - val_accuracy: 0.9375\n",
      "Epoch 9/10\n",
      "100/100 [==============================] - 6s 64ms/step - loss: 0.1030 - accuracy: 0.9598 - val_loss: 0.1570 - val_accuracy: 0.9385\n",
      "Epoch 10/10\n",
      "100/100 [==============================] - 6s 65ms/step - loss: 0.0909 - accuracy: 0.9659 - val_loss: 0.1661 - val_accuracy: 0.9315\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "logdir = './data/keras_model/' + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
    "\n",
    "model.compile(\n",
    "        optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
    "        loss=tf.keras.losses.binary_crossentropy,\n",
    "        metrics=[\"accuracy\"]\n",
    "    )\n",
    "history = model.fit(\n",
    "        ds_train, epochs=10, validation_data=ds_test,\n",
    "        callbacks=[tensorboard_callback], workers=4\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 四，评估模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%load_ext tensorboard\n",
    "#%tensorboard --logdir ./data/keras_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No known TensorBoard instances running.\n"
     ]
    }
   ],
   "source": [
    "from tensorboard import notebook\n",
    "notebook.list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "      <iframe id=\"tensorboard-frame-a9887a186ff83078\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-a9887a186ff83078\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          url.port = 6006;\n",
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   "source": [
    "#在tensorboard中查看模型\n",
    "notebook.start(\"--logdir ./data/keras_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_accuracy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>epoch</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.426335</td>\n",
       "      <td>0.7936</td>\n",
       "      <td>0.327124</td>\n",
       "      <td>0.8580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.308212</td>\n",
       "      <td>0.8675</td>\n",
       "      <td>0.250021</td>\n",
       "      <td>0.8970</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.246233</td>\n",
       "      <td>0.8992</td>\n",
       "      <td>0.235499</td>\n",
       "      <td>0.9050</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0.9162</td>\n",
       "      <td>0.186256</td>\n",
       "      <td>0.9270</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.181721</td>\n",
       "      <td>0.9296</td>\n",
       "      <td>0.198068</td>\n",
       "      <td>0.9180</td>\n",
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       "      <th>6</th>\n",
       "      <td>0.163445</td>\n",
       "      <td>0.9381</td>\n",
       "      <td>0.169458</td>\n",
       "      <td>0.9310</td>\n",
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       "      <td>0.9450</td>\n",
       "      <td>0.165507</td>\n",
       "      <td>0.9295</td>\n",
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       "      <th>8</th>\n",
       "      <td>0.120966</td>\n",
       "      <td>0.9552</td>\n",
       "      <td>0.159841</td>\n",
       "      <td>0.9375</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.103205</td>\n",
       "      <td>0.9598</td>\n",
       "      <td>0.156971</td>\n",
       "      <td>0.9385</td>\n",
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       "      <td>0.090624</td>\n",
       "      <td>0.9659</td>\n",
       "      <td>0.166134</td>\n",
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       "           loss  accuracy  val_loss  val_accuracy\n",
       "epoch                                            \n",
       "1      0.426335    0.7936  0.327124        0.8580\n",
       "2      0.308212    0.8675  0.250021        0.8970\n",
       "3      0.246233    0.8992  0.235499        0.9050\n",
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       "5      0.181721    0.9296  0.198068        0.9180\n",
       "6      0.163445    0.9381  0.169458        0.9310\n",
       "7      0.137487    0.9450  0.165507        0.9295\n",
       "8      0.120966    0.9552  0.159841        0.9375\n",
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     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "dfhistory = pd.DataFrame(history.history)\n",
    "dfhistory.index = range(1,len(dfhistory) + 1)\n",
    "dfhistory.index.name = 'epoch'\n",
    "\n",
    "dfhistory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "def plot_metric(history, metric):\n",
    "    train_metrics = history.history[metric]\n",
    "    val_metrics = history.history['val_'+metric]\n",
    "    epochs = range(1, len(train_metrics) + 1)\n",
    "    plt.plot(epochs, train_metrics, 'bo--')\n",
    "    plt.plot(epochs, val_metrics, 'ro-')\n",
    "    plt.title('Training and validation '+ metric)\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(metric)\n",
    "    plt.legend([\"train_\"+metric, 'val_'+metric])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "plot_metric(history,\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 0s 18ms/step - loss: 0.1661 - accuracy: 0.9315\n",
      "0.16613388545811175 0.9315\n"
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    }
   ],
   "source": [
    "#可以使用evaluate对数据进行评估\n",
    "val_loss,val_accuracy = model.evaluate(ds_test,workers=4)\n",
    "print(val_loss,val_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 五，使用模型\n",
    "\n",
    "可以使用model.predict(ds_test)进行预测。\n",
    "\n",
    "也可以使用model.predict_on_batch(x_test)对一个批量进行预测。"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
    {
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       "array([[2.0920932e-03],\n",
       "       [3.0905008e-05],\n",
       "       [9.9755383e-01],\n",
       "       ...,\n",
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     "execution_count": 24,
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    "model.predict(ds_test)"
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  {
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   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[8.3559680e-01]\n",
      " [9.8934972e-01]\n",
      " [9.9977219e-01]\n",
      " [1.7192066e-03]\n",
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      " [6.7218930e-02]\n",
      " [3.2818615e-03]\n",
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      " [7.0707560e-01]\n",
      " [3.8433075e-03]\n",
      " [9.9999142e-01]\n",
      " [9.9301472e-02]\n",
      " [1.4411335e-02]\n",
      " [1.3533185e-04]\n",
      " [7.0215063e-04]], shape=(20, 1), dtype=float32)\n"
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   ],
   "source": [
    "for x, y in ds_test.take(1):\n",
    "    print(model.predict_on_batch(x[0:20]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 六，保存模型\n",
    "\n",
    "推荐使用TensorFlow原生方式保存模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存权重，该方式仅仅保存权重张量\n",
    "model.save_weights('./data/tf_model_weights.ckpt', save_format = \"tf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "WARNING:tensorflow:From /Users/alan/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1781: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "INFO:tensorflow:Assets written to: ./data/tf_model_savedmodel/assets\n",
      "export saved model.\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 0.1661 - accuracy: 0.9315\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.1661335624754429, 0.9315]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 保存模型结构与模型参数到文件,该方式保存的模型具有跨平台性便于部署\n",
    "\n",
    "model.save('./data/tf_model_savedmodel', save_format=\"tf\")\n",
    "print('export saved model.')\n",
    "\n",
    "model_loaded = tf.keras.models.load_model('./data/tf_model_savedmodel')\n",
    "model_loaded.evaluate(ds_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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